Today, investment decisions in the financial markets require careful analysis of information available from multiple sources. To meet this challenge, financial institutions typical maintain very large datasets that provide a foundation for this analysis. For example, forecasting stock market, currency exchange rate, bank bankruptcies, understanding and managing financial risk, trading futures, credit rating, loan management, bank customer profiling, and money laundering analyses all require large datasets of information for analysis. The datasets of information can be structured datasets as well as unstructured data sets.
Typically, the datasets of information are used to model one or more different entities, each of which may have a relationship with other entities. For example, a company entity may be impacted by, and thereby have a relationship with, any of the following entities: a commodity (e.g., aluminum, corn, crude oil, sugar, etc.), a currency (e.g., euro, sterling, yen, etc.), and one or more competitor. Any change in one entity can have an impact on another entity. For example, rising crude oil prices can impact a transportation company's revenues, which can affect the company's valuation.
Given the quantity and nature of these datasets, each modeled entity tends to have multiple relationships with a large number of other entities. As such, it is difficult to identify which entities are more significant than others for a given entity.
Accordingly, there is a need for systems and techniques to automatically analyze all available data and assign significance scores to entity relationships.